# Observability

Observability is the ability to inspect how an AI system is behaving through its inputs, outputs, actions, errors, and performance signals.

## Why it matters

AI observability connects model behavior with the surrounding application: prompts, retrieved context, tool calls, latency, cost, errors, and user outcomes. Logs alone are insufficient unless teams can trace a result across the whole workflow.

## Example

An operations dashboard groups failures by model version, tool, customer workflow, and error type, with links to privacy-safe traces for investigation.

## FAQ

### What is Observability in simple terms?

Observability is the ability to inspect how an AI system is behaving through its inputs, outputs, actions, errors, and performance signals.

### Why does Observability matter for teams using AI?

AI observability connects model behavior with the surrounding application: prompts, retrieved context, tool calls, latency, cost, errors, and user outcomes. Logs alone are insufficient unless teams can trace a result across the whole workflow.

### What is a practical example of Observability?

An operations dashboard groups failures by model version, tool, customer workflow, and error type, with links to privacy-safe traces for investigation.


Source: https://www.luffy.so/ai-glossary/observability

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